Pratap, Abhishek

The efficacy of smartphone‐based mental health interventions for depressive symptoms: a meta‐analysis of randomized controlled trials

Firth, J., Torous, J., Nicholas, J., Carney, R., Pratap, A., Rosenbaum, S., & Sarris, J. (2017) · World Psychiatry

Digital Mental Health InterventionsMobile Health and mHealth Applications

Cited by 1,097

The mPower study, Parkinson disease mobile data collected using ResearchKit

Bot, B. M., Suver, C., Neto, E. C., Kellen, M., Klein, A., Bare, C., Doerr, M., Pratap, A., Wilbanks, J., Dorsey, E. R., et al. (2016) · Scientific Data

Mobile Health and mHealth ApplicationsContext-Aware Activity Recognition Systems

Cited by 630

Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants

Pratap, A., Neto, E. C., Snyder, P., Stepnowsky, C., Elhadad, N., Grant, D., Mohebbi, M. H., Mooney, S., Suver, C., Wilbanks, J., et al. (2020) · npj Digital Medicine

Mobile Health and mHealth ApplicationsEthics in Clinical Research

Cited by 410

Towards a consensus around standards for smartphone apps and digital mental health

Torous, J., Andersson, G., Bertagnoli, A., Christensen, H., Cuijpers, P., Firth, J., Haim, A., Hsin, H., Hollis, C., Lewis, S., et al. (2019) · World Psychiatry

Digital Mental Health InterventionsMobile Health and mHealth Applications

Cited by 325

The accuracy of passive phone sensors in predicting daily mood

Pratap, A., Atkins, D. C., Renn, B. N., Tanana, M. J., Mooney, S. D., Anguera, J. A., & Areán, P. A. (2019) · Depression and Anxiety

Mental Health Research TopicsDigital Mental Health Interventions

Cited by 119

The Emerging Imperative for a Consensus Approach Toward the Rating and Clinical Recommendation of Mental Health Apps

Torous, J., Firth, J., Huckvale, K., Larsen, M. E., Cosco, T. D., Carney, R., Chan, S., Pratap, A., Yellowlees, P., Wykes, T., et al. (2018) · Journal of Nervous & Mental Disease

Digital Mental Health InterventionsMobile Health and mHealth Applications

Cited by 101

Detecting the impact of subject characteristics on machine learning-based diagnostic applications

Chaibub Neto, E., Pratap, A., Perumal, T. M., Tummalacherla, M., Snyder, P., Bot, B. M., Trister, A. D., Friend, S. H., Mangravite, L., & Omberg, L. (2019) · npj Digital Medicine

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 83

Association of Demographic and Socioeconomic Indicators With the Use of Wearable Devices Among Children

Kim, E. H., Jenness, J. L., Miller, A. B., Halabi, R., de Zambotti, M., Bagot, K. S., Baker, F. C., & Pratap, A. (2023) · JAMA Network Open

Mobile Health and mHealth ApplicationsEthics in Clinical Research

Cited by 53